[go: up one dir, main page]
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing Makes it Possible: Prediction and Evaluation of Natural...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 15545

Special Issue Editors

Prof. Dr. Zhongtai He

E-Mail Website
Guest Editor
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: seismic disasters prevention; structural geomorphology; earthquake seismology; photogrammetry and remote sensing; earthquake emergency response
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenliang Jiang

E-Mail Website
Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: comprehensive remote sensing observation technology; remote sensing of active faults and tectonic landforms; visible remote sensing; InSAR and LiDAR technology; earthquake and geological hazards investigation; emergency observation technology of natural disasters
Dr. Dong Li

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR data processing and application; simultaneous localization and mapping; aerial photogrammetry
Dr. Erick Mas

E-Mail Website
Guest Editor
International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
Interests: multi-agent systems and agent-based simulation; tsunami simulation; evacuation simulation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Disasters have always accompanied human society. The progress of modern civilization has made populations and wealth more concentrated, which is more likely to produce significant losses, secondary disasters, and even chain effects in the face of major disasters. For example, the earthquake and tsunami disaster in Japan on March 11, 2011, caused a large number of casualties as well as property losses and led to secondary disasters, such as nuclear power plant leakage. Disasters have become a key factor threatening the sustainable development of humankind. Remote sensing can obtain global observation data from multi-band, multi-time, and all-weather angles and has the ability of global observation, which is irreplaceable in disaster monitoring. In recent years, the spatial resolution of remote sensing has been rapidly improved, the recognition accuracy has been gradually enhanced, and the time of the repeated observation of ground objects has been continuously shortened. Remote sensing technology has been widely used in the monitoring, assessment, and early warning of disasters. Remote sensing technology is mainly used in earthquakes, landslides, droughts, climate change, and other disasters.

Furthermore, remote sensing data processing methods are the research hotspot because it poses various challenges. Remote sensing technology provides strong technical support for predicting and evaluating disasters. The deep coupling of remote sensing coordination monitoring and emergency response technology systems can significantly reduce the impact of disasters on human beings. We encourage the contribution of remote sensing technology to predicting and evaluating disasters, such as earthquakes, tsunamis, typhoons, rainstorms, hazes, sandstorms, droughts, forest and grassland fires, snow disasters, and floods.

Prof. Dr. Zhongtai He
Prof. Dr. Wenliang Jiang
Dr. Dong Li
Dr. Erick Mas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural hazards
  • remote sensing
  • earthquake hazards
  • geological disaster
  • floods and droughts
  • forest and grassland fires
  • meteorological disaster
  • agricultural disaster
  • emergency and rescue
  • prediction and evaluation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (13 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

27 pages, 56757 KiB  
Article
Active Fault Interpretation in the Northern Segment of the Red River Fault Based on Multisource Remote Sensing Data
by Long Guo, Zhongtai He, Zhikun Ren, Xingao Li and Linlin Li
Remote Sens. 2024, 16(21), 3925; https://doi.org/10.3390/rs16213925 - 22 Oct 2024
Viewed by 833
Abstract
High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection [...] Read more.
High-resolution topographic and geomorphic data are important basic data for the study of active structures. Here, multisource remote sensing data were used to reinterpret the active faults in the northern segment of the Red River Fault (China). First, we obtained airborne light detection and ranging (LiDAR) data, high-resolution GaoFen-7 (GF-7) remote sensing image data, and historical aerial photographs, and a high-resolution digital elevation model (DEM) was generated based on the airborne LiDAR data and GF-7 data. According to the remote sensing interpretation, the main active faults were identified. We subsequently verified the faults in the field and constrained the geographic locations. The current activity was confirmed to be dominantly normal faulting, with some dextral strike-slip components, and the latest active age was the Late Holocene. It reflects the coordination of structural deformation between the rotation of the secondary block and the sliding of the boundary fault within the Sichuan–Yunnan Block. The results show that airborne LiDAR and GF-7 remote sensing data have a great application value in providing high-resolution topographic and geomorphologic data for the study of active structures. The comprehensive application of multisource remote sensing data can greatly improve the reliability of active fault interpretations and provide a reference for follow-up research within the study area. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Geographical location map of the study area: (<b>a</b>) geographical location map of the study area in the context of the Tibetan Plateau; (<b>b</b>) enlarged map of the geographical location of the study area in the context of the Sichuan–Yunnan Block; and (<b>c</b>) geological structure map of the study area. SB: Sichuan Basin; S-YB: Sichuan–Yunnan Block; ICB: Indochina Block; SCB: South China Block; AFZ: Altyn Tagh Fault; HYF: Haiyuan Fault; JLF: Jiali Fault; KLF: Kunlun Fault; XXFZ: Xianshuihe–Xiaojiang Fault Zone; YLPF: Yueliangping Fault; W-QF: Weixi–Qiaohou Fault; N-WF: Nanjian–Weishan Fault; JSJF: Jinshajiang Fault; Z-L-QF: Zhongdian–Longpan–Qiaohou Fault; RRF: Red River Fault; D-Z-DF: Deqin–Zhongdian–Daju Fault; YLXSF: Yulongxueshan Fault; L-XF: Lijiang–Xiaojinhe Fault; CHF: Chenghai Fault; and D-PF: Doupo–Pupeng Fault.</p> Full article ">Figure 2
<p>Methodological workflow chart.</p> Full article ">Figure 3
<p>The coverage range map of each remote sensing dataset obtained. F1-1: Yousuo Basin section of Cangshan Piedmont Fault; F1-2: Dali Basin section of Cangshan Piedmont Fault and Fengyi–Dingxiling Fault; and F3: Midu Basin Margin Fault.</p> Full article ">Figure 4
<p>The obtained point cloud data: (<b>a</b>) the point cloud data of the Cangshan Piedmont Fault; and (<b>b</b>) the point cloud data of the Midu Basin Margin Fault.</p> Full article ">Figure 5
<p>The lithological map of active faults in the study area. F1: Cangshan Piedmont Fault; F2: Fengyi–Dingxiling Fault; and F3: Midu Basin Margin Fault. Stratum: 1, Holocene alluvial sediment; 2, Pleistocene alluvial sediments; 3, Tertiary; 4, Cretaceous; 5, Jurassic; 6, Triassic; 7, Permian; 8, Silurian; 9, Carboniferous; 10, Devonian; 11, Ordovician; 12, Pre-Ordovician Cangshan Group; 13, Neoproterozoic; 14, Palaeoproterozoic; 15, grey limestone; 16, brown schist; 17, syenite porphyry; 18, granite porphyry; 19, diorite; 20, granite; 21, gabbro; 22, mafic rock; 23, diabase gabbro; and 24 lake (the active faults and geology is modified from [<a href="#B22-remotesensing-16-03925" class="html-bibr">22</a>]).</p> Full article ">Figure 6
<p>Linear features of remote sensing interpretation and field verification of the Yousuo Basin section of the Cangshan Piedmont Fault: (<b>a</b>) historical aerial photograph; (<b>b</b>) hillshaded DEM generated from airborne LiDAR; (<b>c</b>) fault triangular faces from Google Earth; (<b>d</b>) photograph of the fault profile; (<b>e</b>) photograph of the fault plane; and (<b>f</b>) photograph of the vertical slickensides (The red arrow represents where the fault passes, and the star represents the geographic location of the Figures).</p> Full article ">Figure 7
<p>The remote sensing interpretation of fault scarps and offset gullies in the Dali Basin section of the Cangshan Piedmont Fault: (<b>a</b>,<b>b</b>) historical aerial photographs from the 1980s showing fault tracks; (<b>c</b>) hillshaded DEM generated from airborne LiDAR showing fault tracks; and (<b>d</b>–<b>h</b>) topographic profiles (The red arrow represents where the fault passes; AA’, BB’, CC’, DD’, and EE’ in (<b>c</b>) are five topographic profiles).</p> Full article ">Figure 8
<p>The remote sensing interpretation of a fault pass and fault scarp in the Dali Basin section of the Cangshan Piedmont Fault: (<b>a</b>) DOM generated from GF-7 showing fault tracks; (<b>b</b>) hillshaded DEM generated from GF-7 showing fault tracks; (<b>c</b>) hillshaded DEM generated from airborne LiDAR showing fault tracks; (<b>d</b>) photograph of the fault pass; and (<b>e</b>) photograph of the fault scarp and fault trough.</p> Full article ">Figure 9
<p>Linear features identified during the remote sensing interpretation of the Dali Basin section of the Cangshan Piedmont Fault: (<b>a</b>) DOM generated from GF-7 showing fault tracks; (<b>b</b>) hillshaded DEM generated from GF-7 showing fault tracks; and (<b>c</b>) hillshaded DEM generated from airborne LiDAR showing fault tracks (The red arrow represents where the fault passes).</p> Full article ">Figure 10
<p>Results of the horizontal offset measurement by LaDiCaoz_v2 software: (<b>a</b>) DEM near R1 generated from airborne LiDAR data; (<b>b</b>) gully R1 location and fault trace; (<b>c</b>) dislocation recovery results of R1; (<b>d</b>) DEM near R2 generated from UAV data; (<b>e</b>) gully R2 location and fault trace; (<b>f</b>) dislocation recovery results of R2; (<b>g</b>) DEM near R3 generated from airborne LiDAR data; (<b>h</b>) gully R3 location and fault trace; and (<b>i</b>) dislocation recovery results of R3 (the middle light blue lines in <a href="#remotesensing-16-03925-f007" class="html-fig">Figure 7</a>b,e,h represent fault traces, and the yellow lines connected by red dots and dark blue dots on both sides are the locations of the gullies; the blue lines in <a href="#remotesensing-16-03925-f007" class="html-fig">Figure 7</a>c,f,i represent the gully position after the displacement recovery).</p> Full article ">Figure 11
<p>Linear features in remote sensing interpretations of the Fengyi–Dingxiling Fault: (<b>a</b>) remote sensing images from Google Earth; (<b>b</b>) DOM generated from GF-7; (<b>c</b>) hillshaded DEM generated from GF-7; and (<b>d</b>) historical aerial photograph (The red arrow represents where the fault passes).</p> Full article ">Figure 12
<p>Field verification of the linear characteristics of the Fengyi–Dingxiling Fault: (<b>a</b>,<b>b</b>) photographs of the fault troughs; (<b>c</b>) photograph of the outcrop with faulted strata; (<b>d</b>) interpretative geological section; (<b>e</b>) photograph of the faulted outcrop; and (<b>f</b>) photograph of the fault spring (The red arrow represents where the fault passes).</p> Full article ">Figure 13
<p>Linear characteristics in remote sensing interpretations and field verification of the Midu Basin Margin Fault: (<b>a</b>) remote sensing images from Google Earth; (<b>b</b>) hillshaded DEM generated from airborne LiDAR data; (<b>c</b>) photograph of the fault profile; (<b>d</b>) interpretative geological section profile; (<b>e</b>) photograph of the fault sag pond; (<b>f</b>,<b>g</b>) photograph of the knickpoint; and (<b>h</b>) field photograph of the bedrock profile (The red arrow represents where the fault passes).</p> Full article ">Figure 14
<p>Linear characteristics in remote sensing interpretations and field verification of the Midu Basin Margin Fault: (<b>a</b>) remote sensing images from Google Earth; (<b>b</b>) hillshaded DEM generated from airborne LiDAR data; (<b>c</b>,<b>d</b>) photograph of fault scarps; and (<b>e</b>,<b>f</b>) photograph of the fault fracture zone (The red arrow represents where the fault passes).</p> Full article ">Figure 15
<p>Formation mechanism of the tension zone in the northern segment of the Red River Fault: (<b>a</b>) model diagram of Sichuan–Yunnan Block movement and boundary fault slip; and (<b>b</b>) simple model diagram of the dynamic mechanism of the northern segment. Y-GF: Yushu–Ganzi Fault; XXHF: Xianshuihe Fault; ZMHF: Zemuhe Fault; XJF: Xiaojiang Fault; LTF: Litang Fault; ANHF: Anninghe Fault; JSJF: Jinshajiang Fault; L-XF: Lijiang–Xiaojinhe Fault; NJF: Nujiang Fault; LCJF: Lancangjiang Fault; W-QF: Weixi–Qiaohou Fault; CHF: Chenghai Fault; N-CF: Nanhua–Chuxiong Fault; S-JF: Shipping–Jianshui Fault; and QJF: Qujiang Fault.</p> Full article ">
22 pages, 64724 KiB  
Article
Characteristics and Tectonic Implications of the Geomorphic Indices of the Watersheds Around the Lijiang–Jinpingshan Fault
by Yongqi Chen, Rui Ding, Shimin Zhang, Dawei Jiang, Luyao Li and Diwei Hua
Remote Sens. 2024, 16(20), 3826; https://doi.org/10.3390/rs16203826 - 14 Oct 2024
Viewed by 826
Abstract
The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on [...] Read more.
The Lijiang–Jinpingshan fault (LJF) is an important secondary boundary fault that obliquely cuts the Sichuan–Yunnan rhombic block. It is of great significance for understanding the tectonic evolution of the Sichuan–Yunnan rhombic block and even the southeastern margin of the Tibet Plateau. Based on a digital elevation model (DEM), this work combines ArcGIS with MATLAB script programs to extract geomorphic indices including slope, the relief degree of the land surface (RDLS), hypsometric integral (HI), and channel steepness index (ksn) of 593 sub–watersheds and strip terrain profiles around the LJF. By analyzing the spatial distribution characteristics of the geomorphic indices and combining the regional lithology and precipitation conditions, the spatial distribution of the geomorphic indices around the study area was analyzed to reveal the implications of the LJF’s activity. The results of this work indicate that (1) the distribution of geomorphic indices around the LJF may not be controlled by climate and lithological conditions, and the LJF is the dominant factor controlling the geomorphic evolution of the region. (2) The spatial distribution patterns of geomorphic indices and strip terrain profiles reveal that the vertical movement of the LJF resulted in a pronounced uplift on its northwest side, with tectonic activity gradually diminishing from northeast to southwest. Furthermore, based on the spatial distribution characteristics of these geomorphic indices, the activity intensity of the LJF can be categorized into four distinct segments: Jianchuan–Lijiang, Lijiang–Ninglang, Ninglang–Muli, and Muli–Shimian. (3) The activity of the LJF obtained from tectonic geomorphology is consistent with the conclusions obtained in previous geological and geodesic studies. This work provides evidence of the activity and segmentation of the LJF in tectonic geomorphology. The results provide insight for the discussion of tectonic deformation and earthquake disaster mechanisms in the southeastern margin of the Tibet Plateau. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>The geological map of the study area. (<b>b</b>) is the red frame range in (<b>a</b>). (<b>a</b>) The main active faults in the southeastern margin of the Tibetan plateau. (<b>b</b>) The geometric distribution of the LJF and introduction of the study area. A–A’ to F–F’ are strip terrain profiles. (1): Qiangtang block, (2)–1: Aba sub–block, (2)–2: Longmenshan sub–block, (3): Chaidamu block, (4): South China block, (5)–1: Northwestern Sichuan sub–block, (5)–2: Central Yunnan sub–block, (6): Jinggu–Ximeng block, (7): Baoshan sub–block, EKL–F: East Kunlun fault, NMWQM–F: North margin of the West Qinling Mountains fault, LRB–F: Longriba fault, MJ–F: Minjiang fault, LMS–F: Longmenshan fault, QC–F: Qingchuan fault, YG–F: Yushu Ganzi fault, XSH–F: Xianshuihe fault, ANH–F: Anninghe fault, ZMH–F: Zemuhe fault, DLS–F: Daliangshan fault, XJ–F: Xiaojiang fault, LT–F: Litang fault, JSJ–F: Jinshajiang fault, DZD–F: Deqin Zhongdian Daju fault, RR–F: Red River fault, NJ–F: Nujiang fault, DL–F: Daluo fault, NTH–F: Nantinghe fault, LR–F: Longling Ruili fault, H E–F: Heqing Eryuan fault, E Y–F: Eastern Piedmont fault of the Yulong Mountains.</p> Full article ">Figure 2
<p>The results of watershed extraction in the study.</p> Full article ">Figure 3
<p>The calculation principle of the analysis window (taking a 3 × 3 size as an example).</p> Full article ">Figure 4
<p>The slope (<b>a</b>) and RDLS (<b>b</b>) distribution around the LJF.</p> Full article ">Figure 5
<p>The spatial distribution of the average slope (<b>a</b>) and RDLS (<b>b</b>) in the sub–watersheds around the LJF.</p> Full article ">Figure 6
<p>The spatial distribution of HI in the sub–watersheds around the LJF.</p> Full article ">Figure 7
<p>The spatial distribution of the average <span class="html-italic">k<sub>sn</sub></span> in the sub–watersheds around the LJF. (<b>b</b>,<b>c</b>) are the purple frame areas in (<b>a</b>). (<b>b</b>) is a display of <span class="html-italic">k<sub>sn</sub></span> in the Lancang River Basin. (<b>c</b>) is a display of <span class="html-italic">k<sub>sn</sub></span> in the Yangtze River Basin.</p> Full article ">Figure 8
<p>The strip terrain profiles across the LJF. The location of (<b>A</b>,<b>A′</b>) to (<b>F</b>,<b>F′</b>) are shown in <a href="#remotesensing-16-03826-f001" class="html-fig">Figure 1</a>. The thick red line accompanied by an upward arrow signifies a reverse fault, whereas the thick grey line with a downward arrow denotes a normal fault. The circles containing a dot on the left side and a cross on the right side indicate that the fault is a left-lateral strike–slip fault.</p> Full article ">Figure 9
<p>The spatial distribution of AAP in the sub–watersheds around the LJF from 2000 to 2022.</p> Full article ">Figure 10
<p>The Spearman correlation between geomorphic indices (slope, RDLS, HI, <span class="html-italic">k<sub>sn</sub></span>), AAP, and RH in the sub–watersheds of the LJF. Note: ** and * denote significance at the 0.01 and 0.05 probability levels, respectively.</p> Full article ">Figure 11
<p>The spatial distribution of strata around the LJF.</p> Full article ">Figure 12
<p>The spatial distribution of the average RH in the sub–watersheds around the LJF.</p> Full article ">Figure 13
<p>The geomorphic indices (slope (<b>a</b>), RDLS (<b>b</b>), HI (<b>c</b>), <span class="html-italic">k<sub>sn</sub></span> (<b>d</b>)) from southwest to northeast in the sub–watersheds on both sides of the LJF.</p> Full article ">Figure 14
<p>A comparison of geomorphic indices on both sides of the LJF.</p> Full article ">
18 pages, 3771 KiB  
Article
Tectonic Activity Analysis of the Laji-Jishi Shan Fault Zone: Insights from Geomorphic Indices and Crustal Deformation Data
by Yujie Ma, Weiliang Huang, Jiale Zhang, Yan Wang, Dong Yu and Baotian Pan
Remote Sens. 2024, 16(20), 3770; https://doi.org/10.3390/rs16203770 - 11 Oct 2024
Viewed by 1171
Abstract
Fault segmentation plays a critical role in assessing seismic hazards, particularly in tectonically complex regions. The Laji-Jishi Shan Fault Zone (LJSFZ), located on the northeastern margin of the Tibetan Plateau, is a key structure that accommodates regional tectonic stress. This study integrates geomorphic [...] Read more.
Fault segmentation plays a critical role in assessing seismic hazards, particularly in tectonically complex regions. The Laji-Jishi Shan Fault Zone (LJSFZ), located on the northeastern margin of the Tibetan Plateau, is a key structure that accommodates regional tectonic stress. This study integrates geomorphic indices, cross-fault deformation rate profiles, and 3D crustal electrical structure data to analyze the varying levels of tectonic activity across different segments of the LJSFZ. We extracted 160 drainage basins along the strike of the LJSFZ from a 30 m resolution digital elevation model and calculated geomorphic indices, including the hypsometric integral (HI), stream length-gradient index (SL), and channel steepness index (ksn), to assess the variations in tectonic activity intensity along the strike of the LJSFZ. The basins were categorized based on river flow directions to capture potential differences across the fault zone. Our results show that the eastern basins of the LJSFZ exhibit the strongest tectonic activity, demonstrated by significantly higher SL and ksn values compared to other regions. A detailed segmentation analysis along the northern Laji Shan Fault and eastern Jishi Shan Fault identified distinct fault segments characterized by variations in SL and ksn indices. Segments with high SL values (>500) correspond to higher crustal uplift rates (~3 mm/year), while segments with lower SL values exhibit lower uplift rates (~2 mm/year), as confirmed by cross-fault deformation profiles derived from GNSS and InSAR data. This correlation demonstrates that geomorphic indices effectively reflect fault activity intensity. Additionally, 3D crustal electrical structure data further indicate that highly conductive mid- to lower-crustal materials originating from the interior of the Tibetan Plateau are obstructed at segment L3 of the LJSFZ. This obstruction leads to localized intense uplift and enhanced fault activity. These findings suggest that while the regional stress–strain pattern of the northeastern Tibetan Plateau is the primary driver of the segmented activity along the Laji-Jishi Shan belt, the direction of localized crustal flow is a critical factor influencing fault activity segmentation. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Tectonic background and topographic features of the LJSFZ, northeastern Tibetan Plateau. (<b>a</b>) Major tectonic structures across the Tibetan Plateau (modified from Huang (2019) [<a href="#B29-remotesensing-16-03770" class="html-bibr">29</a>]), with the white square indicating the area shown in (<b>b</b>). (<b>b</b>) Color-shaded relief map compiled using active fault and earthquake information of the northeastern Tibetan Plateau. The active faults’ data are from Chinese Seismic Intensity Zoning Map (GB18306-2015) [<a href="#B30-remotesensing-16-03770" class="html-bibr">30</a>] and Zhang (2012) [<a href="#B31-remotesensing-16-03770" class="html-bibr">31</a>]. Earthquake locations is from Cheng et al. (2017) [<a href="#B32-remotesensing-16-03770" class="html-bibr">32</a>]. Abbreviations: NRYSF: North Riyue Shan Fault; SRYSF: Sorth Riyue Shan Fault; WQLF: West Qinling Fault.</p> Full article ">Figure 2
<p>(<b>a</b>,<b>b</b>) The lithologic and drainage basin distribution map of the LJSFZ region (lithological information modified after Fu et al., 2018 [<a href="#B46-remotesensing-16-03770" class="html-bibr">46</a>]; the fault information is consistent with the legend in <a href="#remotesensing-16-03770-f001" class="html-fig">Figure 1</a>b). (1. Mafic-ultramafic rock; 2. diorite; 3. granite; 4. peridotite; 5. Hualong Complex: gneiss, schist, and amphibolite; 6. Qingshipo Formation: phyllite and limestone; 7. Dongchagou Formation: schist, phyllite, and quartzite; 8. Huashishan Group: dolomite and limestone; 9. Cambrian volcano-sedimentary series; 10. Ordovician volcanic and sedimentary rocks; 11. Silurian sandstone and conglomerate; 12. Permian sedimentary rock; 13. Triassic sedimentary rock; 14. Jurassic–Quaternary sedimentary rock). The black dashed line represents the boundary between the northern-southern and eastern-western divisions. (<b>c</b>,<b>d</b>) The 4 km wide swath profiles of A-A′ and B-B′. The shaded area represents the range between the maximum and minimum elevation of the topographic profile, while the red line indicates the fault location.</p> Full article ">Figure 3
<p>Geomorphic indices’ distribution maps of the LJSFZ (with different colors representing varying levels of activity) and box-and-whisker diagrams (showing the means, medians, interquartile ranges, and data ranges). (<b>a</b>) <span class="html-italic">HI</span> distribution map, (<b>b</b>) <span class="html-italic">SL</span> distribution map and segmentation of fault activity along the northern and eastern sides of the LJFSZ, (<b>c</b>) <span class="html-italic">k<sub>sn</sub></span> distribution map.</p> Full article ">Figure 4
<p>Comparison of geomorphic indices <span class="html-italic">HI</span>, <span class="html-italic">SL</span>, and <span class="html-italic">k</span><sub>sn</sub> on the northern Laji Shan and the eastern Jishi Shan faults; the red and yellow horizontal, dashed lines in panels. (<b>a</b>–<b>c</b>) represent the boundaries between strong, moderate, and weak tectonic activity based on El Hamdouni et al. [<a href="#B49-remotesensing-16-03770" class="html-bibr">49</a>]; the blue dashed line represents the location of the Yellow River; (<b>a</b>) variation of <span class="html-italic">HI</span> values along the mountain strike; (<b>b</b>) variation of <span class="html-italic">SL</span> values along the mountain strike; (<b>c</b>) variation of <span class="html-italic">k<sub>sn</sub></span> values along the mountain strike; (<b>d</b>) variations in the strike of the northern Laji Shan and eastern Jishi Shan faults.</p> Full article ">Figure 5
<p>Vertical crustal deformation profiles across different segments of the LJSFZ; vertical velocity data from Wu et al., 2024 [<a href="#B43-remotesensing-16-03770" class="html-bibr">43</a>] and precipitation data: (<b>a</b>) location of the profiles and the average annual precipitation (mm/year) in the study area from 1970 to 2000 (obtained from: <a href="https://www.worldclim.org" target="_blank">https://www.worldclim.org</a>, accessed on 26 September 2024); (<b>b</b>) seven vertical crustal deformation profiles. In the topographic profiles, red, orange, and green represent high, medium, and low <span class="html-italic">SL</span> values for the drainage basins the profiles’ cross, respectively. Red and black fault lines indicate Holocene active faults and Cenozoic faults. The black dots in the deformation profiles represent vertical uplift rates, with the purple line showing the fitted result of these points. The black dashed line indicates the average uplift rate for the maximum and minimum portions of the profile, while the red and gray squares represent the error ranges.</p> Full article ">Figure 6
<p><span class="html-italic">SL</span> Values and Vu (uplift rates) linear fit.</p> Full article ">Figure 7
<p>Crustal deformation field and electrical structure: (<b>a</b>) GNSS velocity field (horizontal black arrows) and leveling data (vertical yellow arrow) from Zhuang et al., 2023 [<a href="#B65-remotesensing-16-03770" class="html-bibr">65</a>], along with subsurface electrical structure at 10 km depth from Zhao et al., 2022 [<a href="#B38-remotesensing-16-03770" class="html-bibr">38</a>], The white dashed line is the profile line; (<b>b</b>) left-lateral strike-slip movement along the Laji Shan fault; (<b>c</b>) cross-section A-A′ topographic profile, adapted from [<a href="#B46-remotesensing-16-03770" class="html-bibr">46</a>], the lithology fill is consistent with <a href="#remotesensing-16-03770-f002" class="html-fig">Figure 2</a>. HCL refers to the high-conductivity layer, HRB refers to the high-resistivity body, and NLJSF refers to the North Laji Shan fault.</p> Full article ">
19 pages, 15362 KiB  
Article
Deep Tectonic Environment Analysis of the Lingshan Conjugate Earthquake within the Qinzhou Fold Belt, South China: Insights Derived from 3D Resistivity Structure Model
by Chunheng Yan, Bin Zhou, Yan Zhan, Xiangyu Sun, Sha Li, Lei Li and Peilan Guo
Remote Sens. 2024, 16(19), 3740; https://doi.org/10.3390/rs16193740 - 9 Oct 2024
Viewed by 1148
Abstract
The Qinzhou fold belt, situated at the contact zone between the Yangtze and Cathaysia blocks in South China, was affected by the 1936 Lingshan M6¾ earthquake and the 1958 Lingshan M5¾ earthquake, both of which occurred within the conjugate structure. Understanding the deep [...] Read more.
The Qinzhou fold belt, situated at the contact zone between the Yangtze and Cathaysia blocks in South China, was affected by the 1936 Lingshan M6¾ earthquake and the 1958 Lingshan M5¾ earthquake, both of which occurred within the conjugate structure. Understanding the deep seismogenic setting and causal mechanism of the Lingshan conjugate earthquake is of great significance for assessing the seismic disaster risk in the region. In this study, we utilized 237 magnetotelluric datasets and employed three-dimensional electromagnetic inversion to characterize the deep-seated three-dimensional resistivity structure of the Qinzhou fold belt and the Lingshan seismic zone. The results reveal that: (1) The NE-trending faults within the Qinzhou fold belt and adjacent areas are classified as trans-crustal faults. The faults exhibit crust-mantle ductile shear zones in their deeper sections, which are essential in governing regional tectonic deformation and seismic activity; (2) The electrical structure of the Qinzhou fold belt is in line with the tectonic characteristics of a composite orogenic belt, having experienced several phases of tectonic modification. The southeastern region is being influenced by mantle-derived magmatic activities originating from the Leiqiong area over a significant distance; (3) In the Lingshan seismic zone, the NE-trending Fangcheng-Lingshan fault is a trans-crustal fault and the NW-trending Zhaixu fault is an intra-crustal fault. The electrical structure pattern “two low, one high” in the zone has a significant impact on the deep tectonic framework of the area and influences the deformation behavior of shallow faults; and (4) The seismogenic structure of the 1936 Lingshan M6¾ earthquake was the Fangcheng-Lingshan fault. The earthquake’s genesis was influenced by the coupling effect of tectonic stress and deep thermal dynamics. The seismogenic structure of the 1958 Lingshan M5¾ earthquake was the Zhaixu fault. The earthquake’s genesis was influenced by tectonic stress and static stress triggering from the 1936 Lingshan M6¾ earthquake. The conjugate rupture mode in the Lingshan seismic zone is influenced by various factors, including differences in physical properties, rheology of deep materials, and the scale and depth of fault development. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Geotectonic diagrams of SCB and its neighboring zones and topography, fault, and magnetotelluric (MT) points distribution maps in the study area. (<b>a</b>) Geotectonic diagrams of SCB and its adjacent zones (modified from [<a href="#B26-remotesensing-16-03740" class="html-bibr">26</a>,<a href="#B27-remotesensing-16-03740" class="html-bibr">27</a>]). North China Block (NCB), South China Block (SCB), Yangtze Block (YB), Cathaysian Block (CB), Songpan-Ganzi Block (SGB), Indochina Block (IB), South China Sea Block (SCSB), Qinling-Dabie Orogen belt (QDOB) [<a href="#B26-remotesensing-16-03740" class="html-bibr">26</a>]; Qinzhou Bay-Hangzhou Bay Tectonic Junction Zone (QHTJZ [<a href="#B27-remotesensing-16-03740" class="html-bibr">27</a>]). (<b>b</b>) Topography map of Qinzhou Fold Belt (QZFB), and fault structure and MT line distribution maps in the study area. Youjiang Rift Basin (YJRB), Xianggui Rift Basin (XGRB), Yunkai Uplift (YKU). Western Fangcheng-Lingshan fault (FLf1), Eastern Fangcheng-Lingshan fault (FLf2), Western Lingshan-Tengxian fault (LTf1), Eastern Lingshan-Tengxian fault (LTf2), Northern Nandan-Kunlunguan fault (NKf1), Southern Nandan-Kunlunguan fault (NKf2), Western Hepu-Beiliu fault (HBf1), Eastern Hepu-Beiliu fault (HBf2), Zhaixu fault (ZXf), Guilin-Nanning fault (GNf), Pingxiang-Dali fault (PDf), Baise-Hepu fault (BHf), Dongzhong-Xiaodong fault (DXf), Pubei fault (PBf), Muzi fault (MZf). Segmenting of FLf, Fangheng segment (FCS), Pingji segment (PJS), Lingshan segment (LSS), and Shinan segment (SNS). Nanning (NN), Qinzhou (QZ), Fangchenggang (FCG), Guigang (GG), Tengxian (TX), Yulin (YL), Beiliu (BL), Lingshan (LS), Pubei (PB), Bobai (BB), Hepu (HP).</p> Full article ">Figure 2
<p>Geological structure and MT point distribution of the Lingshan earthquake zone and its adjacent areas. Dayaoshan Fault Depression (DYSFD), Shiwandashan Fault Depression (SWDSFD), Lingshan Fold Belt (LSFB), Liuwandashan Uplift (LWDSU), Bobai fold Basin (BBFB). Xiaoyi (XY), Nayang (NY), Nalin (NL), Fuwang (FW), Muzi (MZ), Dongping (DP), Pingshan (PS), Shitang (ST). The names of faults are consistent with those in <a href="#remotesensing-16-03740-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Apparent resistivity and phase curves for typical MT sites on profile L1 (<b>a</b>) and L2 (<b>b</b>) (see <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a> for the locations). Red points denote data in the XY direction, and blue points denote data in the YX direction. The names of construction units and faults are consistent with those in <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>c</b>) Phase tensor ellipses filled with absolute value of skew angle β for periods of 0.1 s, 14 s, 341 s. (<b>d</b>–<b>f</b>) Phase tensor ellipses filled with phase tensor invariant for periods of 0.1 s, 14 s, 341 s.</p> Full article ">Figure 5
<p>RMS Misfit distribution of the 3D inversion.</p> Full article ">Figure 6
<p>Five cross sections extracted from the 3D Model. (See <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a> for the locations of cross sections). The names of construction units and faults are consistent with those in <a href="#remotesensing-16-03740-f001" class="html-fig">Figure 1</a> and <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 7
<p>Electrical structure slices of the Lingshan earthquake zone (see <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a> for the locations). Black lines denote faults, red lines denote coseismic surface rupture zone.</p> Full article ">Figure 8
<p>Electrical structure profiles of the Lingshan earthquake zone. “NE” represents a profile from southwest to northeast, and “L” represents a profile from northwest to southeast. The names of construction units and faults are consistent with those in <a href="#remotesensing-16-03740-f001" class="html-fig">Figure 1</a> and <a href="#remotesensing-16-03740-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 9
<p>(<b>a</b>) GPS velocity field and plain strain rate distribution in the Lingshan earthquake region and its surrounding area. The small arrows indicate the GPS velocities. Error ellipses are 95% confidence interval. (<b>b</b>) 3D electrical structures in the Lingshan earthquake region. (<b>c</b>) The cumulated stress loading on the rupture plane of the 1958 M5¾ Lingshan earthquake associated with the 1936 M6¾ Lingshan earthquake (effective friction coefficient μ′ = 0.4). The black lines indicate coseismic surface rupture zone. (<b>d</b>) Cartoon diagram of conjugate seismic pattern of Lingshan earthquake. Red arrows indicate direction of tectonic stress field, and the black arrows indicate of coseismic displacement.</p> Full article ">
15 pages, 16261 KiB  
Article
Quantifying the Pabu Normal Fault Scarp, Southern Tibetan Plateau: Insights into Regional Earthquake Risk
by Guanghao Ha and Feng Liu
Remote Sens. 2024, 16(18), 3473; https://doi.org/10.3390/rs16183473 - 19 Sep 2024
Viewed by 660
Abstract
The location of the main boundary fault of the Yadong-Gulu Rift (YGR) shifts from the east side in the southern segment to the west side in the northern segment. The Nyemo Graben Group (NGG) connects the southern and northern segments of the YGR [...] Read more.
The location of the main boundary fault of the Yadong-Gulu Rift (YGR) shifts from the east side in the southern segment to the west side in the northern segment. The Nyemo Graben Group (NGG) connects the southern and northern segments of the YGR and provides clues for understanding the migration of boundary fault locations along the YGR. However, the NGG has received very little attention. In this study, we map the geometry of the Pabu normal fault, which is the boundary fault of the westernmost graben in the NGG, using high-resolution remote sensing images. We then utilized a digital elevation model (DEM) with a spatial resolution of 1 m. Morphometric parameters such as scarp height, width, and slope were obtained from elevation profiles in three typical deformation regions. Our results reveal a fault segment approximately 3 km long that links the southern and northern segments of the Pabu Fault. Each fault segment could be a major segment. Furthermore, based on regional tectonic activity, the Pabu Fault has the potential to produce an earthquake with a magnitude of around M 6.7. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) A simplified active tectonics map of Tibet and the Himalayas. The fault data is from [<a href="#B9-remotesensing-16-03473" class="html-bibr">9</a>]. (<b>b</b>) A sketch and segmentation of the Yadong–Gulu Rift (YGR), divided into northern, central, and southern segments from north to south [<a href="#B6-remotesensing-16-03473" class="html-bibr">6</a>]. The NGG, located in the central portion of the Yadong–Gulu Rift and intersecting the Yarlung Tsangpo River, consists of six relatively smaller half-grabens. The earthquake data before 1970 come from [<a href="#B10-remotesensing-16-03473" class="html-bibr">10</a>], while the data after 1970 come from the United States Geological Survey (<a href="https://earthquake.usgs.gov/" target="_blank">https://earthquake.usgs.gov/</a>, accessed on 15 May 2023). These data can be accessed through the Data Sharing Infrastructure of the National Earthquake Data Center, which can be found at <a href="http://data.earthquake.cn" target="_blank">http://data.earthquake.cn</a>, accessed on 20 March 2023.</p> Full article ">Figure 2
<p>(<b>a</b>) The digital elevation model (DEM) derived from GaoFen-7 satellite remote sensing images. Significant linear features can be traced in the DEM (indicated by the red arrow). The blue arrow indicates the observed direction of the Pabu graben in the field. The pink box indicates the location of the fault plane of the Pabu fault in the field. The green box indicates the area where we conducted geological interpretation via remote sensing image. (<b>b</b>) The spatial distribution of active faults and a simplified geological map of the Pabu graben. The two pink lines indicate the locations of geological profiles across the graben.</p> Full article ">Figure 3
<p>The two geological profile maps show that the Pabu graben is a half-graben. The general southeastward inclination of the topography creates space for the Pabu River, which collects tributaries from the northwest mountainous region. The primary boundary and secondary fault displace Middle Pleistocene periglacial sediments, forming the fault scarp. The geological symbols are the same as those in <a href="#remotesensing-16-03473-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>(<b>a</b>) The 3D view of the Pabu fault landform clearly displays its line tectonic geomorphology. The river channel flows across the fault trace and disrupts the continuation of the fault landform. Additionally, the DEM shows terrain distortion points, likely due to data collection errors or changes in the terrain. The red arrow indicates the location of the Pabu fault. (<b>b</b>) A vertical profile along the fault strike, derived from the DEM shown in panel (<b>a</b>), is used to calculate scarp height, width, and slope using SPARTA [<a href="#B21-remotesensing-16-03473" class="html-bibr">21</a>]. Linear regression is applied to the upper and lower surfaces, positioned away from the scarp, to obtain these parameters. (<b>c</b>) The three pink areas highlight typical tectonic deformation regions used to calculate scarp height, width, and slope.</p> Full article ">Figure 5
<p>(<b>a</b>) An alluvial fan formed at the river’s outlet from the mountain and was later incised to create a river terrace T1 and T2, respectively. The red arrows indicate the trace of the Pabu fault. (<b>b</b>) The Pabu fault has displaced the river terrace T1, resulting in the formation of a fault scarp (<b>c</b>) at the site shown in (<b>b</b>). The black arrows show the fault scarps. The scarps are soil mantled.</p> Full article ">Figure 6
<p>(<b>a</b>) Tectonic geomorphology of the Pabu graben. (<b>b</b>) The southern part of the Pabu fault (F3) is characterized by discontinuous and prominent fault scarps. (<b>c</b>) The granite is displaced, exposing the fault striations and cataclasite.</p> Full article ">Figure 7
<p>(<b>a</b>) The typical tectonic deformation region was analyzed morphologically using a DEM with a spatial resolution of 1 m. The red arrow indicates the location of the Pabu fault. Panels (<b>b</b>–<b>d</b>) present the height, width, and slope profiles of the Parbu fault scarp.</p> Full article ">Figure 8
<p>(<b>a</b>) The typical tectonic deformation region was examined morphologically using a DEM with a 1 m spatial resolution. The red arrow indicates the location of the Pabu fault. Panels (<b>b</b>–<b>d</b>) illustrate the height, width, and slope profiles of the Parbu fault scarp.</p> Full article ">Figure 9
<p>(<b>a</b>) The tectonic deformation region was analyzed morphologically using a DEM with a spatial resolution of 1 m. The red arrow indicates the location of the Pabu fault. Panels (<b>b</b>–<b>d</b>) provide detailed height, width, and slope profiles of the Parbu fault scarp.</p> Full article ">
16 pages, 14227 KiB  
Article
Westward Migration of the Chenghai–Jinsha Drainage Divide and Its Implication for the Initiation of the Chenghai Fault
by Shuang Bian, Xibin Tan, Yiduo Liu, Feng Shi and Junfeng Gong
Remote Sens. 2024, 16(18), 3471; https://doi.org/10.3390/rs16183471 - 19 Sep 2024
Cited by 3 | Viewed by 894
Abstract
The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to [...] Read more.
The Chenghai Fault in the Chuan–Dian block terminates at the northwestern segment of the Red River Fault, and is a significant seismogenic structure. The kinematic evolution of this fault should be closely related to the regional tectonic deformation. However, it is difficult to obtain information on structural deformation of the Chenghai Fault due to the large amount of precipitation and well-developed vegetation. The Chenghai normal faulting may drive drainage reorganization in this region, which provides a new perspective for reconstructing and evaluating the tectonic history. High-resolution digital elevation models (DEM) obtained by remote sensing greatly facilitate the study of drainage evolution and active tectonics. We use two methods (χ-plot and Gilbert metrics) to measure the drainage divide stability based on the ALOS DEM (12.5 m resolution) and further reproduce the drainage evolution process in response to the asymmetric uplift by numerical modeling. The results show that the Chenghai–Jinsha drainage divide, hosted by the footwall block of the Chenghai Fault, is migrating westward (away from the Chenghai Fault) and will continue moving ~2.2–3.5 km to reach a steady state. Its migration is controlled by the Chenghai normal faulting. The Chenghai–Jinsha drainage divide formed close to the Chenghai Fault’s surface trace and continues to migrate westward in response to the asymmetric uplift. It only took a few million years for the Chenghai–Jinsha drainage divide to migrate to its current location based on the numerical modeling. The restoration of the drainage reorganization implies that the Chenghai Fault initiated in the Pliocene, which probably results from kinematic reversal along the Red River Fault. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) Schematic tectonic map of the Tibetan Plateau region (compiled from [<a href="#B4-remotesensing-16-03471" class="html-bibr">4</a>]). (<b>B</b>) Major faults in the eastern Tibetan Plateau (compiled from [<a href="#B44-remotesensing-16-03471" class="html-bibr">44</a>]). The light blue lines are the river systems. RRF, Red River Fault; CHF: Chenghai Fault; LMF: Longmenshan Fault; SGF: Sagaing Fault; XJF: Xiangjiang Fault; XSHF: Xianshuihe Fault.</p> Full article ">Figure 2
<p>Perspective views and χ map of channels for the Chenghai–Jinsha drainage divide. The location is shown in <a href="#remotesensing-16-03471-f001" class="html-fig">Figure 1</a>B. (<b>A</b>) Perspective views of channels mapped with <span class="html-italic">k<sub>sn</sub></span>. Arrows indicate the migration results based on the Gilbert metrics method. (<b>B</b>) Map of χ and geology. Blue filling represents sedimentary rocks, yellow filling represents Quaternary sediments, and transparent filling represents igneous rocks. Arrows show the divide migration directions and cross-divide difference in normalized <span class="html-italic">k<sub>sn</sub></span>. (<b>C</b>) χ-plots for nine paired rivers across the divide. Numbers in the χ-plots are the average <span class="html-italic">k<sub>sn</sub></span> values. The results show that the Chenghai–Jinsha drainage divide is moving west.</p> Full article ">Figure 3
<p>(<b>A</b>) Schematic of Gilbert’s (1877) [<a href="#B61-remotesensing-16-03471" class="html-bibr">61</a>] ‘Law of Unequal Declivities’. (<b>B</b>) Reference drainage area used in all metrics for calculating across divide differences (compiled from [<a href="#B33-remotesensing-16-03471" class="html-bibr">33</a>]).</p> Full article ">Figure 4
<p>(<b>A</b>) Perspective views for a captured river on the Chenghai–Jinsha drainage divide. The Chenghai Drainage has just captured the headwaters of the Jinsha Drainage. The location is showed in the white box of the <a href="#remotesensing-16-03471-f002" class="html-fig">Figure 2</a>A. (<b>B</b>) χ-plots for the beheaded and captured channels. They are highlighted as bold lines in (<b>A</b>).</p> Full article ">Figure 5
<p>Divide stability analysis of the Chenghai–Jinsha drainage divide using the Gilbert metrics method [<a href="#B33-remotesensing-16-03471" class="html-bibr">33</a>]. (<b>A</b>) Divide metric histograms for segments <span class="html-italic">ab</span>, <span class="html-italic">bc</span>, and <span class="html-italic">cd</span>. Histograms with black and red rectangles represent watersheds on the western and eastern side of the drainage divide, respectively. (<b>B</b>) Standardized delta plot for the sub-segment of the Chenghai–Jinsha drainage divide. Bars show the standard deviation at 1σ level. Locations of the letters <span class="html-italic">a–d</span> are shown in <a href="#remotesensing-16-03471-f002" class="html-fig">Figure 2</a>A.</p> Full article ">Figure 6
<p>Prediction for the steady-state location of the Chenghai–Jinsha drainage divide. (<b>A</b>) Topography and drainage system. River segments highlighted in dark blue are measured and analyzed. The pink area is the predicted steady-state location. (<b>B</b>) The Hack’s coefficient and exponent (<span class="html-italic">k</span> and <span class="html-italic">b</span>). (<b>C</b>) The relationship diagram between the normalized drainage divide location (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>β</mi> </mrow> </msub> <mo>⁄</mo> <mo>(</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi mathvariant="sans-serif">α</mi> </mrow> </msub> <mo>+</mo> <msub> <mrow> <mi>D</mi> </mrow> <mrow> <mi>β</mi> </mrow> </msub> <mo>)</mo> </mrow> </semantics></math>) and uplift rate ratio (<span class="html-italic">U</span><span class="html-italic"><sub>β</sub></span>/<span class="html-italic">U</span><span class="html-italic"><sub>α</sub></span>). (<b>D</b>) Swath profile A–A′ of topography across the divide. Location of the swath is marked by the yellow line in panel (<b>A</b>). The red arrow represents the direction of drainage divide migration.</p> Full article ">Figure 7
<p>Topographic maps of divide migration in response to different asymmetric uplift patterns. The top illustrations show the setting of uplift rate. All the models show it will only take several million years for the divide to migrate for ~2.5 km (the location of the vertical dashed lines). The timescale decreases as the uplift rate on the eastern edge and the uplift radio increase. When each model was run for 23 Myr, the divide migration distances are greater than 5 km.</p> Full article ">Figure 8
<p>Diagrams of the evolution process of the Chenghai–Jinsha drainage divide. (<b>A</b>) Before the activity of the Chenghai Fault, we inferred that the rivers flowed westward to the Jinsha Drainage. (<b>B</b>) When the normal faulting began, the Chenghai–Jinsha drainage divide first occurred at the eastern edge and migrated westward until reaching a steady state. (<b>C</b>) Schematic of the post-capture profile incision. The modern river profiles are extracted based on DEM, while paleo river profile is speculated. The red arrow represents the direction of drainage divide migration. (<b>D</b>) Elevation profile along the drainage divide (A–A′). The arrows indicate the locations of wind gaps.</p> Full article ">Figure 9
<p>Tectonic evolution of the southeastern Tibetan Plateau (modified after [<a href="#B13-remotesensing-16-03471" class="html-bibr">13</a>]). (<b>A</b>) Affected by the continuous northward indentation of the Indian Plate, the southeastern Tibetan Plateau experienced large-scale lateral extrusion and the Red River Fault synchronously initiated sinistral shearing motion. (<b>B</b>) Fault systems have undergone extensive kinematic reversal. Right-lateral faulting on the Red River Fault and normal faulting in the northern Chuan–Dian block may be the consequences of the present extrusion phase.</p> Full article ">
18 pages, 84736 KiB  
Article
Newly Discovered NE-Striking Dextral Strike-Slip Holocene Active Caimashui Fault in the Central Part of the Sichuan-Yunnan Block and Its Tectonic Significance
by Xin Tan, Kuan Liang, Baoqi Ma and Zhongtai He
Remote Sens. 2024, 16(17), 3203; https://doi.org/10.3390/rs16173203 - 29 Aug 2024
Viewed by 763
Abstract
The Sichuan-Yunnan block is a tectonically active region in China, with frequent large earthquakes occurring in and around it. Despite most earthquakes being concentrated along boundary faults, intraplate faults also have the potential to generate damaging earthquakes. Remote sensing makes it possible to [...] Read more.
The Sichuan-Yunnan block is a tectonically active region in China, with frequent large earthquakes occurring in and around it. Despite most earthquakes being concentrated along boundary faults, intraplate faults also have the potential to generate damaging earthquakes. Remote sensing makes it possible to identify these potential earthquake source faults. During an active fault investigation in the Liangshan area, a distinct lithological boundary named Caimashui fault was found. The geometric distribution and kinematic parameter of the fault is crucial for assessing seismic hazards and understanding the deformation pattern within the Sichuan-Yunnan block. The Caimashui fault is mapped with remote sensing interpretation, a field survey, and UAV measurement. Through trenching and Quaternary dating, the Late Quaternary active characteristics of the fault are studied. The fault is a Holocene active dextral strike-slip fault with a reverse component, exhibiting a dextral strike-slip rate of ~0.70 ± 0.11 mm/a. Paleoseismic investigation shows that the last surface rupture event of the Caimashui fault occurred later than 4150 ± 30a BP, with a magnitude of M ≥ 7.0. The fault may act as a secondary splitting fault, absorbing the deformation caused by various sinistral strike-slip rates of the boundary faults and the potential energy from the counterclockwise rotation of the Central Yunnan micro-block. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Tectonic setting and distribution map of the southeast margin of the Tibetan Plateau. (<b>a</b>) Tectonic location of the study area. Black rectangle shows the study area. ATF: Altyn Tagh fault, QHF: Qilian-Haiyuan fault, KF: Kunlunshan fault, XF: Xianshuihe fault, XJF: Xiaojiang fault, RRF: Red River fault, JLF: Jiali fault, CF: Karakorum fault, HFT: Himalayan Frontal Thrust. (<b>b</b>) Main active tectonics in the study area. The fault locations are modified from [<a href="#B41-remotesensing-16-03203" class="html-bibr">41</a>]. Colored circles represent historically and instrumentally documented earthquakes, which are modified from [<a href="#B34-remotesensing-16-03203" class="html-bibr">34</a>,<a href="#B36-remotesensing-16-03203" class="html-bibr">36</a>,<a href="#B42-remotesensing-16-03203" class="html-bibr">42</a>]. XF: Xianshuihe fault, ANHF: Aninghe fault, ZMHF: Zemuhe fault, DLSF: Daliangshan fault, LMSFZ: Longmenshan fault zone, LJ-XJHF: Lijiang-Xiaojinhe fault, XGDF: Xigeda fault, YMF: Yuanmou fault, CMSF: Caimashui fault, QJF: Qujiang fault, SJF: Shiping-Jianshui fault.</p> Full article ">Figure 2
<p>Geological features and fault distributions near the Caimashui fault. Lithological data are obtained from 1:200,000 geologic maps (<a href="https://www.ngac.org.cn" target="_blank">https://www.ngac.org.cn</a>).</p> Full article ">Figure 3
<p>Tectonic landforms caused by the Caimashui fault near Huoshi Town (see location in <a href="#remotesensing-16-03203-f002" class="html-fig">Figure 2</a>). (<b>a</b>) Satellite image (from Google Earth) of the fault trace. (<b>b</b>) Tectonic landforms around the Sanjiaozhuang site. (<b>c</b>) Tectonic landforms around the Xiaochacun site. (<b>d</b>) Tectonic landforms around the Tangjiawan site. (<b>e</b>) Tectonic landforms around the Huoshi Town site. For locations, see <a href="#remotesensing-16-03203-f003" class="html-fig">Figure 3</a>a. Red arrows and red lines indicate the location of the fault.</p> Full article ">Figure 4
<p>Tectonic landforms around the Xiaochacun trench; see location in <a href="#remotesensing-16-03203-f003" class="html-fig">Figure 3</a>c. (<b>a</b>) Shaded relief map (from UAV-derived DEM) and interpreted map, showing the fault scarp, fault trough, and offset terrace. (<b>b</b>) Aerial image showing the tectonic landforms along the fault. (<b>c</b>) Field photo of the offset terrace. (<b>d</b>) Field photo of the ground fissures. (<b>e</b>) Aerial photo of the Xiaochacun trench, with broken white lines indicating fault scarps. (<b>f</b>) Field photo of the fault scarp.</p> Full article ">Figure 5
<p>(<b>a</b>) Photo mosaic and (<b>b</b>) interpreted map of the west wall of the Xiaochacun trench. (<b>c</b>) Photo mosaic and (<b>d</b>) interpreted map of the east wall of the Xiaochacun trench. Black lines indicate the stratigraphic contacts between units. Red lines indicate the fault planes. Black dots show the locations of the radiocarbon samples, labeled with their corresponding calibrated ages.</p> Full article ">Figure 6
<p>Sketch maps showing the formation and evolution of sag ponds in Xiaochacun. (<b>a</b>,<b>b</b>) Ridges and gullies before the formation of the sag ponds; (<b>c</b>,<b>d</b>) show the fault displacing the ridges, leading to the formation of sag ponds 1 and 2; (<b>e</b>,<b>f</b>) show the gullies cutting through the sag ponds, resulting in the abandonment of the sag ponds and their subsequent displacement by ongoing fault activity.</p> Full article ">Figure 7
<p>Geometrical and tectonic model of the Sichuan-Yunnan tectonic zone. (<b>a</b>) Fault slip rates and the distribution of strong earthquakes on the Sichuan-Yunnan block. (<b>b</b>) A cartoon model showing that the slip rate difference between the left-slip faults and right-slip faults inside the block leads to counterclockwise rotation (modified from [<a href="#B8-remotesensing-16-03203" class="html-bibr">8</a>]). WYMB: West Yunnan microblock; CYMB: Central Yunnan microblock; NCF: Nanhua-Chuxiong fault; JSJF: Jinshajiang fault; SJF: Shiping-Jianshui fault; QJF: Qujiang fault.</p> Full article ">
19 pages, 22743 KiB  
Article
The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China
by Feibiao Huo, Fuyun Guo, Pengqing Shi, Ziyan Gao, Yan Zhao, Yongbin Wang, Xingmin Meng and Dongxia Yue
Remote Sens. 2024, 16(15), 2817; https://doi.org/10.3390/rs16152817 - 31 Jul 2024
Cited by 3 | Viewed by 1058
Abstract
The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. [...] Read more.
The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Geology of the Shuimo catchment. (1. talus deposit; 2. landslide deposit; 3. debris flow transportation zone and deposit; 4. upper Pleistocene Malan Loess; 5. Triassic sandy slate and sandstone; 6. lower Permian limestone and slate; 7. landslide; 8. debris flow; 9. collapse; 10. river system; 11. stratigraphic boundary; 12. catchment boundary).</p> Full article ">Figure 2
<p>Vegetation coverage of the Shuimo catchment.</p> Full article ">Figure 3
<p>The overall process framework.</p> Full article ">Figure 4
<p>Flow chart of SBAS-InSAR.</p> Full article ">Figure 5
<p>DEM (<b>left</b>) and slope map (<b>right</b>) of the Shuimo catchment.</p> Full article ">Figure 6
<p>InSAR interpretation of the material sources in the Shuimo catchment.</p> Full article ">Figure 7
<p>Interpretation of the material sources in the Shuimo catchment.</p> Full article ">Figure 8
<p>Typical landslides in the Shuimo catchment.</p> Full article ">Figure 9
<p>Channel deposits and erosion phenomena in the Shuimo catchment.</p> Full article ">Figure 10
<p>Distribution of slope angles within the Shuimo catchment.</p> Full article ">Figure 11
<p>Spatial distribution of heavy rainfall in Wenxian County on 17 August 2020.</p> Full article ">Figure 12
<p>Temporal distribution of rainfall in the Shuimo catchment from 1 August to 19 August 2020.</p> Full article ">Figure 13
<p>Temporal distribution of 24 h rainfall in the Shuimo catchment (from 15:00 on 16 August, to 15:00 on 17 August 2020).</p> Full article ">Figure 14
<p>Blockage points in the Shuimo catchment.</p> Full article ">Figure 15
<p>Images taken before and after the 2020 “17.8” debris flow in the Shuimo catchment.</p> Full article ">Figure 16
<p>Interpretation of the area of the alluvial fan of the Shuimo catchment debris flow.</p> Full article ">Figure 17
<p>Pre- and post-disaster images of Shuimo New Village in the middle part of the Shuimo catchment.</p> Full article ">
23 pages, 20141 KiB  
Article
Spatial Variations of Late Quaternary Slip Rates along the Ganzi–Xianshuihe Fault Zone in the Eastern Tibet
by Kai Sun, Chuanyou Li, Mingjian Liang, Xinnan Li, Quanxing Luo, Guangxue Ren, Feipeng Huang and Junjie Li
Remote Sens. 2024, 16(14), 2612; https://doi.org/10.3390/rs16142612 - 17 Jul 2024
Viewed by 823
Abstract
The Ganzi–Xianshuihe Fault Zone is a large-scale sinistral strike-slip fault zone on the eastern Tibet. As the boundary fault zone of the Bayankala Block and the Chuandian Block, it controls the clockwise rotation of the southeastern Tibet. However, there is still controversy regarding [...] Read more.
The Ganzi–Xianshuihe Fault Zone is a large-scale sinistral strike-slip fault zone on the eastern Tibet. As the boundary fault zone of the Bayankala Block and the Chuandian Block, it controls the clockwise rotation of the southeastern Tibet. However, there is still controversy regarding the activity changes between fault zones. Therefore, accurately determining the slip rates of faults in the area is crucial for characterizing regional plate motions and assessing associated seismic hazards. We focused on studying four fault segments near the Ganzi–Xianshuihe Fault Zone, including the Manigango, Ganzi, Luhuo, and Daofu segments. In each segment, we selected typical sinistral piercing points and carried out Unmanned Aerial Vehicle (UAV) photogrammetry to obtain high-resolution terrain data. We utilized LaDiCaoz_V2.2 and GlobalMapper software (LaDiCaoz_V2.2 and Global Mapper v17.0) to measure the offsets, together with optically stimulated luminescence (OSL) dating, to constrain the timing of fault activity. The estimated slip rates for the Manigango, Ganzi, Luhuo, and Daofu segments are as follows: 9.2 ± 0.75 mm/yr, 9.59 ± 1.7 mm/yr, 4.23 ± 0.66 mm/yr, and 7.69 ± 0.76 mm/yr, respectively. Integrating previous results with slip rates estimated in this study, our analysis suggests the slip rate of the Ganzi–Xianshuihe Fault Zone is around 8–10 mm/year, exhibiting a consistent slip rate from northwest to southeast. This reflects the overall coordination of the movement on the eastern Tibet, with the strike-slip fault zone only controlling the direction of movement. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Location map showing the Ganzi Yushu Xianshuihe fault and the study area. (<b>a</b>): Location map of the research area relative to the Tibet. (<b>b</b>): Distribution map of active faults in western Sichuan. The terrain data with 2 km resolution is derived from the global topographic relief data provided by Generic Mapping Tools 6.5. Black and grey lines indicate active fault traces from the Seismic Active Fault Survey Data Center (<a href="http://www.activefault-datacenter.cn/" target="_blank">http://www.activefault-datacenter.cn/</a>, accessed on 12 June 2024). Blue dots represent historical earthquakes. The seismic data are from the China Earthquake Information Network. QB: Qaidam block; BHB: Bayan Har block; LMSF: Longmenshan fault; GZYSF: Ganzi–Yushu fault; XSHF: Xianshuihe fault; DLSF: Daliangshan fault; ANHF: Anninghe fault; ZMHF: Zemuhe fault; XJF: Xiaojiang fault; RRF: Red River fault.</p> Full article ">Figure 2
<p>Ganzi Yushu Xianshuihe fault strip map [<a href="#B16-remotesensing-16-02612" class="html-bibr">16</a>,<a href="#B17-remotesensing-16-02612" class="html-bibr">17</a>,<a href="#B19-remotesensing-16-02612" class="html-bibr">19</a>,<a href="#B21-remotesensing-16-02612" class="html-bibr">21</a>,<a href="#B22-remotesensing-16-02612" class="html-bibr">22</a>,<a href="#B23-remotesensing-16-02612" class="html-bibr">23</a>,<a href="#B24-remotesensing-16-02612" class="html-bibr">24</a>,<a href="#B25-remotesensing-16-02612" class="html-bibr">25</a>,<a href="#B26-remotesensing-16-02612" class="html-bibr">26</a>,<a href="#B27-remotesensing-16-02612" class="html-bibr">27</a>,<a href="#B28-remotesensing-16-02612" class="html-bibr">28</a>], featuring the distribution of slip rate research (represented by points); ‘Be10’ represents the cosmogenic nuclide, ‘estimate’ refers to the age range roughly judged by regional sediment strata, ‘TL’ refers to thermoluminescence, and ‘C14’ refers to radiocarbon ages.</p> Full article ">Figure 3
<p>Manigangowangqing gully dislocation map (99.13°E, 31.97°N; 3953 m). (<b>a</b>) field landform map; The red arrow indicates the location of the fault, the white dashed line indicates the edge of the terrace, and the blue arrow indicates the direction of the river; (<b>b</b>) Geomorphological surface interpretation map based on 0.3 m resolution digital elevation hillshade image; The black lines indicate the location of the terrain profile, the red arrows indicate the relative direction of fault movement, and the red circles with cross symbols indicate the sampling location. (<b>c</b>,<b>d</b>) Sampling point photo and interpretation map.</p> Full article ">Figure 4
<p>Dislocation measurement and restoration map of Wangqing site. (<b>a</b>) In the corresponding geomorphic interpretation of the Wangqing site. The black thin line indicates a contour line with a 3-meter interval; the white dotted line indicates the edge of the terrace; (<b>b</b>) The red dot line indicates the terrace edge of the upper wall of the fault, and the blue dot line indicates the terrace edge of the lower wall of the fault; (<b>c</b>) 87.1 m slip-back recovery diagram of fault; (<b>d</b>) The topographic profiles on both sides of the fault.</p> Full article ">Figure 5
<p>Measurement and recovery of point dislocation in Shengkang Township of Ganzi Fault. (<b>a</b>) Hillshade map of the high-resolution digital elevation model of Shengkang site; (<b>b</b>) Shengkang landform surface interpretation map. The red boxes represent the locations where age samples were collected in previous studies [<a href="#B19-remotesensing-16-02612" class="html-bibr">19</a>]. The red line indicates the location of the fault, the black dotted line indicates the edge of the terrace, and the blue line indicates the direction of the river. Light gray line indicates contour line; (<b>c</b>,<b>d</b>) Photos and interpretation maps of SK-OSL-21 sampling point in T2 terrace, the black circle and white cross indicate the position of the sample, the black point indicates fine silt, and the white circle indicates gravel; (<b>e</b>,<b>f</b>) Photo and interpretation map of SK-OSL-05 sampling point in T3 terrace.</p> Full article ">Figure 6
<p>Measurement and recovery of point dislocation in Shengkang site of Ganzi Fault. (<b>a</b>) The fault trace line of Shengkang site of Ganzi Fault, the red dot line indicates the terrace edge of the upper wall of the fault, and the blue dot line indicates the terrace edge of the lower wall of the fault.; (<b>b</b>) 465 m slip-back recovery diagram of fault. The white dot line indicates the terrace edge.</p> Full article ">Figure 7
<p>Geomorphic map of Luhuo fault Kale site (100.25°E, 31.68°N; 3512 m). (<b>a</b>) Geomorphological surface interpretation map of Kale site, black lines indicate the location of the terrain profile, red line indicates the location of the fault, the red arrows indicate the relative direction of fault movement, and the black circles with cross symbols indicate the sampling location. The white dot line indicates the terrace edge; blue line indicates the direction of the river; (<b>b</b>) The topographic profiles on both sides of the fault; (<b>c</b>,<b>d</b>) Photo and interpretation map of GD-OSL-25 sampling point in the T2 terrace,the white circle indicates gravel; (<b>e</b>–<b>g</b>) Field photo, and photo and interpretation map of GD-OSL-22 sampling point in the T3 terrace.</p> Full article ">Figure 8
<p>Measurement and recovery of point dislocation in Shengkang site of Ganzi Fault. (<b>a</b>) The fault trace line of Kallie site of Luhuo fault. The white dot line indicates the terrace edge, contour interval is 10 m; (<b>b</b>) 117.77 m slip-back recovery diagram of fault.</p> Full article ">Figure 9
<p>Geomorphic map of Daofu fault Mazi site (101.05°E, 31.03°N; 3125 m). (<b>a</b>) Field geomorphological photos of Mazi site, red line indicates the location of the fault, the red arrows indicate the relative direction of fault movement, and the black circles with cross symbols indicate the sampling location. The white dot line indicates the terrace edge; (<b>b</b>) Topographic surface interpretation map, blue line indicates the direction of the river; (<b>c</b>,<b>d</b>) sampling point photo and interpretation map; (<b>e</b>) 426 m slip-back recovery diagram of fault (blue indicates the location of the gully river).</p> Full article ">Figure 10
<p>Slip rate distribution diagram. (<b>a</b>) The previous slip rate comparison diagram; different colored circles represent different dating methods, red box represents the results obtained by using the OSL dating method, and the numbers in the sliding rate box represent the reference numbers of different researchers. (<b>b</b>) Slip rate research point distribution map, red line indicates the location of the main fault [<a href="#B16-remotesensing-16-02612" class="html-bibr">16</a>,<a href="#B17-remotesensing-16-02612" class="html-bibr">17</a>,<a href="#B19-remotesensing-16-02612" class="html-bibr">19</a>,<a href="#B21-remotesensing-16-02612" class="html-bibr">21</a>,<a href="#B22-remotesensing-16-02612" class="html-bibr">22</a>,<a href="#B23-remotesensing-16-02612" class="html-bibr">23</a>,<a href="#B24-remotesensing-16-02612" class="html-bibr">24</a>,<a href="#B25-remotesensing-16-02612" class="html-bibr">25</a>,<a href="#B26-remotesensing-16-02612" class="html-bibr">26</a>,<a href="#B27-remotesensing-16-02612" class="html-bibr">27</a>,<a href="#B28-remotesensing-16-02612" class="html-bibr">28</a>].</p> Full article ">
20 pages, 14793 KiB  
Article
Comprehensive Study on the 143 A.D. West Gangu Earthquake in the West Qinling Area, Northeastern Margin of Tibetan Plateau
by Ruihuan Su, Daoyang Yuan, Hong Xie, Aiguo Wang, Yameng Wen, Jinchao Yu, Yanwen Chen, Hongqiang Li, Hao Sun and Lijun Zhang
Remote Sens. 2024, 16(12), 2109; https://doi.org/10.3390/rs16122109 - 11 Jun 2024
Cited by 1 | Viewed by 1123
Abstract
The 143 A.D. west Gangu earthquake is documented to have occurred in the West Qinling area, which is located on the northeastern margin of the Tibetan Plateau. Initial limited historical records suggest the earthquake took place along the West Qinling fault (WQLF) in [...] Read more.
The 143 A.D. west Gangu earthquake is documented to have occurred in the West Qinling area, which is located on the northeastern margin of the Tibetan Plateau. Initial limited historical records suggest the earthquake took place along the West Qinling fault (WQLF) in the western region of Gangu County. However, the absence of corresponding geological and geomorphological evidence has posed a considerable challenge in accurately quantifying parameters such as the precise location, magnitude, and seismogenic fault segment in earlier investigations. In this study, a comprehensive examination of multiple residual surface rupture zones within the macroseismic zone of this earthquake enabled the determination of the seismogenic structure, magnitude, and rupture zone scale through diverse methodologies, which include field geological investigations, chronology testing, Unmanned Aerial Vehicle (UAV) aerial surveying, and interpretation of landslides along the fault zone. The results reveal that the seismogenic structure of this seismic event is associated with the Zhangxian fault segment of the WQLF, also marked by a dense distribution of large landslides from Zhangxian to Yuanyangzhen. The epicenter was identified at the eastern end of the Zhangxian fault segment of the WQLF. Furthermore, the magnitude of the 143 A.D. west Gangu earthquake is estimated to be approximately Ms 7–7.3, with the residual surface rupture zone intermittently extending over about 22 km and a maximum horizontal dislocation along the rupture zone of 2.8 ± 0.5 m. This detailed investigation contributes foundational insights for further evaluating the seismic risk across various segments of the WQLF. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Distribution map of major active faults in Tibetan Plateau (<b>a</b>) and active faults in the northeastern margin of the Tibetan Plateau (<b>b</b>). ATF: Altyn Tagh fault; QL–HYF: Qilian–Haiyuan fault; EKLF: East Kunlun fault; GZ–YSF: Ganzi–Yushu fault; MYXSF: Mayaxueshan fault; GLF: Gulang fault; RYSF: Riyueshan fault; ELSF: Elashan fault; QHNSF: Qinghainanshan fault; GHNSF: Gonghenanshan fault; LJSF: Lajishan fault; LPSF: Liupanshan fault; WQLF: West Qinling fault; GDF: Guide fault; LT–DCF: Lintan–Dangchang fault; LLF: Lixian–Luojiabao fault; BLJF: Bailongjiang fault; LJF: Liangdang–Jiangluo fault; MJF: Minjiang fault; MD–GDF: Maduo–Gande fault; ZTF: Zhongtie fault. Red arrows indicate the horizontal relative motion direction. S1–S4 indicates the segmentation of the WQLF. The base map is based on 30 m DEM of USGS [<a href="#B12-remotesensing-16-02109" class="html-bibr">12</a>], fault data are modified from Deng et al. [<a href="#B13-remotesensing-16-02109" class="html-bibr">13</a>] and Xu et al. [<a href="#B14-remotesensing-16-02109" class="html-bibr">14</a>], earthquake data are from the National Earthquake Data Center [<a href="#B15-remotesensing-16-02109" class="html-bibr">15</a>].</p> Full article ">Figure 2
<p>Regional geological situation and geometric distribution of the WQLF in the study area. Pz<sub>1</sub>: Lower Paleozoic Niutouhe Group; D<sub>3</sub>: Upper Devonian series; C<sub>1</sub>: Lower Carboniferous series; P<sub>1</sub>: Lower Permian series; P<sub>2</sub>: Upper Permian series; T: Triassic series; K: Cretaceous series; E: Paleogene series; N: Neogene series; Q<sub>3</sub>: Upper Pleistocene series; Q<sub>4</sub><sup>1</sup>–Q<sub>4</sub><sup>2</sup>: Holocene series; ∑<sub>5</sub><sup>1</sup>: serpentinized ultrabasic rock; γ<sub>5</sub><sup>1</sup>: medium-grained diorite. Sites ①–⑦ are the investigate areas of surface rupture zones. The stratigraphic information is modified from the 1:200,000 geological map.</p> Full article ">Figure 3
<p>Principles of UAV SfM technology (<b>a</b>) modified from Wei et al. [<a href="#B25-remotesensing-16-02109" class="html-bibr">25</a>], and field survey photo of the Phantom 4 RTK (<b>b</b>).</p> Full article ">Figure 4
<p>UAV image (<b>a</b>) and field survey photos (<b>b</b>,<b>c</b>) of the surface rupture zone east of Pangjiawan village. Red and white arrows indicate the horizontal relative motion direction.</p> Full article ">Figure 5
<p>Sampling profile and dislocation models of faulted gully east of Pangjiawan village. (<b>a</b>) Profile on hanging wall of the gully. (<b>b</b>) Profile on footwall of the gully. (<b>c</b>) Planar dislocation model of the gully. (<b>d</b>) Stereoscopic dislocation model of the gully. Red arrows indicate the horizontal relative motion direction.</p> Full article ">Figure 6
<p>UAV image of the surface rupture zone (<b>a</b>,<b>b</b>) and field survey photos (<b>c</b>–<b>e</b>) southwest of Baishitou village. Red and lavender arrows indicate the horizontal relative motion direction.</p> Full article ">Figure 7
<p>Sampling profile (<b>a</b>) and sketch (<b>b</b>) of the side wall of the faulted gully southwest of Baishitou village. Red arrows indicate the motion direction.</p> Full article ">Figure 8
<p>(<b>a</b>) Satellite image of spatial distribution of the WQLF in the south of Heiniuping–Yanjiaping village. (<b>b</b>) Surface rupture zone in the south of Heiniuping village. (<b>c</b>) Surface rupture zone and fault profile in Yanjiaping village. (<b>d</b>) Detail features of the fault section in <a href="#remotesensing-16-02109-f008" class="html-fig">Figure 8</a>c. Red arrows indicate the horizontal relative motion direction. Yellow circle symbols represent the direction of relative motion, the circle centered on a dot indicates the outward movement.</p> Full article ">Figure 9
<p>Lateral wall profile of the trench across the surface rupture zone in Yanjiaping village. Yellow circle symbols represent the direction of relative motion, the circle centered on a dot indicates the outward movement.</p> Full article ">Figure 10
<p>Satellite image (<b>a</b>) and field survey photo (<b>b</b>) of the surface rupture zone east of Qingyawan. Red arrows indicate the horizontal relative motion direction.</p> Full article ">Figure 11
<p>Spatial distribution map of landslide interpreted by visual interpretation (<b>a</b>) and UAV photos of landslides proofread in the field (<b>b</b>,<b>c</b>). The black dotted box represents the landslide field investigation area. Red and purple arrows indicate the horizontal relative motion direction. Blue lines indicate the rivers.</p> Full article ">Figure 12
<p>Histogram of landslide areas’ classification in the visual interpretation region and pie charts of landslide areas’ classification in the dense landslide area.</p> Full article ">Figure 13
<p>Spatial distribution of surface rupture zone and coseismic dislocations. The yellow area represents the range of surface displacement values observed in this earthquake.</p> Full article ">Figure 14
<p>Segmentation and structural geometry and historical earthquakes along the WQLF (modified after Shao et al. [<a href="#B16-remotesensing-16-02109" class="html-bibr">16</a>]). The ellipse represents severe damage in areas caused by historical earthquakes. Red arrows indicate the horizontal relative motion direction.</p> Full article ">
25 pages, 27480 KiB  
Article
A Bayesian Approach for Forecasting the Probability of Large Earthquakes Using Thermal Anomalies from Satellite Observations
by Zhonghu Jiao and Xinjian Shan
Remote Sens. 2024, 16(9), 1542; https://doi.org/10.3390/rs16091542 - 26 Apr 2024
Cited by 2 | Viewed by 1287
Abstract
Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential [...] Read more.
Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Figure 1

Figure 1
<p>Global seismically active regions with earthquake counts in 1° × 1° grids. Gray areas represent 5° × 5° grids surrounding central grids containing multiple earthquakes. The subgraphs provide zoomed-in views of (<b>A</b>) the Mediterranean region, (<b>B</b>) Mainland China and adjacent regions, and (<b>C</b>) Southeast Asia.</p> Full article ">Figure 2
<p>AIRS data products on 31 December 2020, including the geophysical parameters: (<b>a</b>) skin temperature (ST), (<b>b</b>) air temperature (AT), (<b>c</b>) total integrated column water vapor burden (CWV), (<b>d</b>) clear-sky outgoing longwave radiation (OLR), and (<b>e</b>) OLR.</p> Full article ">Figure 3
<p>Component analysis of earthquake forecast probability for OLR anomaly on 31 December 2020. (<b>a</b>) Probability of OLR anomaly occurrence before earthquakes; (<b>b</b>) probability of OLR anomaly occurrence without earthquakes; (<b>c</b>) posterior probability of earthquake occurrence given the OLR anomaly; (<b>d</b>) natural probability of earthquake occurrence; (<b>e</b>) probability gain from OLR anomaly. Data were log10-transformed to emphasize very low values, except for panel (<b>b</b>).</p> Full article ">Figure 4
<p>Component analysis of earthquake forecast probability for OLR anomaly on 31 December 2020, focused on Mainland China and adjacent regions. (<b>a</b>) Probability of OLR anomaly occurrence before earthquakes; (<b>b</b>) probability of OLR anomaly occurrence without earthquakes; (<b>c</b>) posterior probability of earthquake occurrence given the OLR anomaly; (<b>d</b>) natural probability of earthquake occurrence; (<b>e</b>) probability gain from OLR anomaly. See <a href="#app1-remotesensing-16-01542" class="html-app">Supplementary Figures S5–S8</a> for complete analyses of ST, AT, CWV, and COLR anomalies. Data were log10-transformed to emphasize very low values, except for panel (<b>b</b>).</p> Full article ">Figure 5
<p>Component analysis of earthquake forecast probability for OLR anomaly on 31 December 2020, focused on Mediterranean region. (<b>a</b>) Probability of OLR anomaly occurrence before earthquakes; (<b>b</b>) probability of OLR anomaly occurrence without earthquakes; (<b>c</b>) posterior probability of earthquake occurrence given the OLR anomaly; (<b>d</b>) natural probability of earthquake occurrence; (<b>e</b>) probability gain from OLR anomaly. See <a href="#app1-remotesensing-16-01542" class="html-app">Supplementary Figures S9–S12</a> for complete analyses of ST, AT, CWV, and COLR anomalies. Data were log10-transformed to emphasize very low values, except for panel (<b>b</b>).</p> Full article ">Figure 6
<p>Component analysis of earthquake forecast probability for OLR anomaly on 31 December 2020, focused on Southeast Asia. (<b>a</b>) Probability of OLR anomaly occurrence before earthquakes; (<b>b</b>) probability of OLR anomaly occurrence without earthquakes; (<b>c</b>) posterior probability of earthquake occurrence given the OLR anomaly; (<b>d</b>) natural probability of earthquake occurrence; (<b>e</b>) probability gain from OLR anomaly. See <a href="#app1-remotesensing-16-01542" class="html-app">Supplementary Figures S13–S16</a> for complete analyses of ST, AT, CWV, and COLR anomalies. Data were log10-transformed to emphasize very low values, except for panel (<b>b</b>).</p> Full article ">Figure 7
<p>Histograms of probability gains &gt;1 for AIRS multiparametric anomalies on 31 December 2020.</p> Full article ">Figure 8
<p>Optimal combination of five geophysical parameters (i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR) for maximizing earthquake forecast probability. (<b>a</b>) Spatial distribution of each parameter included in optimal combination. (<b>b</b>) Probability gain achieved using optimal parameter combination.</p> Full article ">Figure 9
<p>Time series of occurrence probability of anomalies before earthquakes <math display="inline"><semantics> <mrow> <mi>P</mi> <mfenced separators="|"> <mrow> <mi>A</mi> <mi>n</mi> <mi>o</mi> <mo>|</mo> <mi>E</mi> <mi>Q</mi> </mrow> </mfenced> </mrow> </semantics></math> and occurrence probability of anomalies without earthquakes <math display="inline"><semantics> <mrow> <mi>P</mi> <mfenced separators="|"> <mrow> <mi>A</mi> <mi>n</mi> <mi>o</mi> <mo>|</mo> <mover accent="true"> <mrow> <mi>E</mi> <mi>Q</mi> </mrow> <mo>¯</mo> </mover> </mrow> </mfenced> </mrow> </semantics></math> in Taiwan area for (<b>a</b>) ST, (<b>b</b>) AT, (<b>c</b>) CWV, (<b>d</b>) COLR, and (<b>e</b>) OLR anomalies derived from AIRS products. Shaded areas indicate one standard deviation of <math display="inline"><semantics> <mrow> <mi>P</mi> <mfenced separators="|"> <mrow> <mi>A</mi> <mi>n</mi> <mi>o</mi> <mo>|</mo> <mi>E</mi> <mi>Q</mi> </mrow> </mfenced> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mi>P</mi> <mfenced separators="|"> <mrow> <mi>A</mi> <mi>n</mi> <mi>o</mi> <mo>|</mo> <mover accent="true"> <mrow> <mi>E</mi> <mi>Q</mi> </mrow> <mo>¯</mo> </mover> </mrow> </mfenced> </mrow> </semantics></math> within a 3 × 3 window.</p> Full article ">Figure 10
<p>Time series of posterior probabilities of earthquake forecast based on each parameter in the Taiwan area during 2006–2020.</p> Full article ">Figure 11
<p>Column water vapor (CWV) anomalies and probability indices on 31 December 2021. (<b>a</b>) CWV anomaly calculated by Equation (1). (<b>b</b>) Binary anomalous regions identified using the optimal anomaly recognition criteria. (<b>c</b>) Probability index (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> <mi>I</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>). (<b>d</b>) Probability index (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>). Black dots denote earthquakes with magnitude &gt;5.5 occurring within 30 days after 31 December 2021.</p> Full article ">Figure 12
<p>Time series of skin temperature (ST) anomalies for earthquake EQ15 (<a href="#remotesensing-16-01542-t001" class="html-table">Table 1</a>) from June 2020 to June 2023. (<b>a</b>) ST anomaly calculated by Equation (1). (<b>b</b>) Area ratio of anomalous regions identified using the optimal anomaly recognition criteria within 7° × 7° window. (<b>c</b>) Anomaly index (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> <mi>I</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>). (<b>d</b>) Anomaly index (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> <mi>I</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>). (<b>e</b>) Earthquakes with magnitude ≥ 4.5. The shaded areas indicate standard deviation in 7° × 7° window.</p> Full article ">Figure 13
<p>Ensemble earthquake forecast probabilities and gains on 31 December 2020. (<b>a</b>) Ensemble forecast probability using capped eigenvalue method. (<b>b</b>) Probability gains from ensemble forecast compared to natural earthquake probability. (<b>c</b>) Histogram of probability gains.</p> Full article ">
24 pages, 35052 KiB  
Article
Using Keyhole Images to Map Soil Liquefaction Induced by the 1966 Xingtai Ms 6.8 and 7.2 Earthquakes, North China
by Yali Guo, Yueren Xu, Haofeng Li, Lingyu Lu, Wentao Xu and Peng Liang
Remote Sens. 2023, 15(24), 5777; https://doi.org/10.3390/rs15245777 - 18 Dec 2023
Cited by 1 | Viewed by 1676
Abstract
In March 1966, Ms 6.8 and 7.2 earthquakes occurred in Xingtai, North China, resulting in widespread soil liquefaction that caused severe infrastructure damage and economic losses. Using Keyhole satellite imagery combined with aerial images and fieldwork records, we interpreted and identified 66,442 [...] Read more.
In March 1966, Ms 6.8 and 7.2 earthquakes occurred in Xingtai, North China, resulting in widespread soil liquefaction that caused severe infrastructure damage and economic losses. Using Keyhole satellite imagery combined with aerial images and fieldwork records, we interpreted and identified 66,442 liquefaction points and analyzed the coseismic liquefaction distribution characteristics and possible factors that influenced the Xingtai earthquakes. The interpreted coseismic liquefaction was mainly concentrated above the IX-degree zone, accounting for 80% of all liquefaction points. High-density liquefaction zones (point density > 75 pieces/km2) accounted for 22% of the total liquefaction points. Most of the interpreted liquefaction points were located at the region with a peak ground acceleration (PGA) of >0.46 g. The liquefaction area on 22 March was significantly larger than that on 8 March. The region of liquefaction was mainly limited by sandy soil conditions, water system conditions, and seismic geological conditions and distributed in areas with loose fine sand and silt deposits, a high water table (groundwater level increases before both mainshocks corresponding to the liquefaction intensive regions), rivers, and ancient river channels. Liquefaction exhibited a repeating characteristic in the same region. Further understanding of the liquefaction characteristics of Xingtai can provide a reference for the prevention of liquefaction in northern China. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Distribution of active tectonics and historical earthquakes in North China; historical seismic data from the Catalogue of Historical Strong Earthquakes in China [<a href="#B21-remotesensing-15-05777" class="html-bibr">21</a>]; fault data from the Seismic Activity Fault Prospecting Data Center (<a href="https://www.activefault-datacenter.cn" target="_blank">https://www.activefault-datacenter.cn</a>, accessed on 5 August 2013); Hongdong earthquake isoseismic line [<a href="#B22-remotesensing-15-05777" class="html-bibr">22</a>], Huaxian earthquake isoseismic line [<a href="#B23-remotesensing-15-05777" class="html-bibr">23</a>], Tanlu earthquake isoseismic line [<a href="#B24-remotesensing-15-05777" class="html-bibr">24</a>], Heze earthquake, Bohai earthquake, Haicheng earthquake, Tangshan earthquake isoseismic line [<a href="#B14-remotesensing-15-05777" class="html-bibr">14</a>], and Xingtai earthquake isoseismic line [<a href="#B25-remotesensing-15-05777" class="html-bibr">25</a>]. All the isoseismic lines were produced by using the Chinese intensity scale with 12 levels. The red dashed line indicates the Tangshan–Hejian–Cixian fault, and the yellow dashed line indicates the seismic null zone trace [<a href="#B26-remotesensing-15-05777" class="html-bibr">26</a>]. BJ: Beijing, TJ: Tianjin, TS: Tangshan, HC: Haicheng, DL: Dalian, QHD: Qinhuangdao, JN: Jinan, QD: Qingdao, TC: Tancheng, ZZ: Zhengzhou, XA: Xi’an, TY: Taiyuan, SJZ: Shijiazhuang, XT: Xingtai, HD: Handan, HZ: Heze, TX: Tongxian, SH: Sanhe, TG: Tanggu, HG: Hangu, LT: Laoting, YK: Yingkou, CX: Cixian, HD: Hongdong, FY: Fenyang, and YX: Yingxian. (<b>b</b>) Topographic profile of line segment AB. The blue and red lines represent the maximum elevation and minimum elevation curves of the strip topographic profile, respectively.</p> Full article ">Figure 2
<p>Flowchart of the step-by-step analysis method for identifying and mapping liquefaction using remotely sensed data.</p> Full article ">Figure 3
<p>Extent of distribution of multisource image data in the study area.</p> Full article ">Figure 4
<p>(<b>a</b>) Interpreted coseismic liquefaction distribution map of the 1966 Xingtai earthquakes based on the Chinese intensity scale. (<b>b</b>) Density map of the interpreted liquefaction around the epicenter area. XH: Xiaohe River; BSH: Beishahe River; WH: Wuhe River; ZH: Zhihe River; BLH: Beilihe River; NLH: Nanlihe River; XZH: Xiaozhanghe River; FYH: Fuyanghe River; ZYH: Ziyahe River.</p> Full article ">Figure 5
<p>Interpreted soil liquefaction of the (<b>a</b>) northernmost site and (<b>b</b>) southernmost site; locations are as shown in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 6
<p>(<b>a</b>) Distribution of villages where liquefaction was detected after the 8 March and 22 March earthquakes; BHZ: Beihouzhuang; DW: Dongwang; XLZ: Xinlizhuang; GZQ: Gengzhuangqiao; XJH: Xujiahe; HZT: Huangzhaotai; NJQ: Niujiaqiao; BYZ: Beiyanzhuang; MEZ: Maoerzhai; LSX: LiShenxian; CL: Changlu; XZ: Xianzhuang; JJZ: Jingjiazhuang; DLZ: Donglizhai; PAC: Ping’ancun; and HC: Hucun. Names of rivers are given in the notes for <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>. (<b>b</b>) Map of the route taken by Gao Weiming during the post-earthquake field trips (Institute of Earthquake Prediction of the China Earthquake Administration, 2019). ZT: Zaotuo; SZGD: Shizigeda; JC: Jiucheng; BJZ: Baijiazhuang; ML: Malan; DJZ: Dujiazhuang; SY: Sunyao; SJZ: Shijiazui; PT: Poutou; and AXZ: Aixinzhuang.</p> Full article ">Figure 7
<p>Frequency statistics of liquefaction points along inferred deep faults (counted in 10 km intervals) and the vertical strike (counted in 5 km intervals). The locations of profiles CD and EF are shown in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 8
<p>(<b>a</b>) Liquefaction phenomenon around Ren Village, with the specific location shown in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>; (<b>b</b>) lateral spreading phenomenon; (<b>c</b>) liquefaction along the remains of the ancient river channel; (<b>d</b>) sandblasted holes in the village; and (<b>e</b>) liquefaction phenomenon near the riverbank.</p> Full article ">Figure 9
<p>Distribution features of liquefaction points shown on the image. (<b>a</b>) Haphazard and scattered point-like distribution; (<b>b</b>) bead-like and line-like distribution; and (<b>c</b>) block or sheet-like distribution. Red arrows show the locations of coseismic soil liquefaction points during mainshocks.</p> Full article ">Figure 10
<p>Distribution of the interpreted liquefaction points and ancient river channels. Ancient river channel traces are from [<a href="#B50-remotesensing-15-05777" class="html-bibr">50</a>]. The specific location is shown in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 11
<p>Map of the relationship between the distribution of villages where liquefication occurred and groundwater level rose and fell before the 8 March and 22 March earthquakes. Data on the changes in groundwater level were obtained from the 1966 Xingtai Earthquake Fact Sheet (Institute of Earthquake Physics under the China earthquake administration, 1986). Names of the rivers are given in the notes in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>.</p> Full article ">Figure 12
<p>(<b>a</b>) Kernel density map of houses that collapsed in the 8 March earthquake; (<b>b</b>) kernel density map of houses that collapsed in the 22 March earthquake; and (<b>c</b>) total Kernel density map of houses that collapsed in the two earthquakes. Names of rivers are given in the notes in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>. Village abbreviations, JJK: Jiajiakou; DW: Dongwang; AXZ: Aixinzhuang; LZZ: Lianzizhen; DGY: Dongguoying; GT: Guanting; XZC: Xizhangcun; HZC: Hanzhuangcun; and GZQ: Gengzhuangqiao.</p> Full article ">Figure 13
<p>(<b>a</b>) Kernel density map of earthquake casualties on 8 March 1966; (<b>b</b>) kernel density map of earthquake casualties on 22 March 1966. Names of rivers are given in the notes in <a href="#remotesensing-15-05777-f004" class="html-fig">Figure 4</a>. MEZ: Maoerzhai; BYZ: Beiyanzhuang; ML: Malan; RC: Rencun; HEY: Huangerying; DYS: Dayingshang; and SZL: Sizhilan.</p> Full article ">Figure 14
<p>(<b>a</b>) Comparison of the building extent in villages within the high-intensity zone of the Xingtai earthquake in 1966 and 2023; (<b>b</b>) comparison of images of the building extent in the vicinity of the epicenter of the earthquake on 22 March 1966; and (<b>c</b>) statistical histogram of the building area in 1966 and 2023.</p> Full article ">Figure 15
<p>(<b>a</b>) Images of the range of buildings in Niujiaqiao village in 1966 and 2023 at the locations shown in <a href="#remotesensing-15-05777-f014" class="html-fig">Figure 14</a>; (<b>b</b>) UAV photo of Niujiaqiao village near the epicenter of the earthquake on 8 March 1966 (taken on 27 February 2023), with most of the extended houses built as bungalows with no seismic-resistant measures and few of the school and residential buildings exhibiting seismic-resistant structures; (<b>c</b>) two-story houses next to streets with no seismic-resistant measures; (<b>d</b>) houses built around the adjacent dried-up river channel with no seismic-resistant structures; and (<b>e</b>) new construction of a small number of houses with structural columns.</p> Full article ">

Other

Jump to: Research

13 pages, 5668 KiB  
Technical Note
Two Sets of High-Conductivity Systems with Different Scales Reveal the Seismogenic Mechanism of Earthquakes in the Songyuan Area, Northeastern China
by Xiaodong Jia, Zhuoyang Li, Jiangtao Han, Hesheng Hou, Zhonghua Xin, Lijia Liu and Wenyu Liu
Remote Sens. 2024, 16(3), 547; https://doi.org/10.3390/rs16030547 - 31 Jan 2024
Viewed by 1063
Abstract
To reveal the deep seismogenic environment and mechanism of earthquakes in Songyuan City, Northeastern China, 59 broadband magnetotelluric sites in the Songyuan area were arranged in this study at a spacing of 5 km. In addition, two intersecting magnetotelluric profiles, with a total [...] Read more.
To reveal the deep seismogenic environment and mechanism of earthquakes in Songyuan City, Northeastern China, 59 broadband magnetotelluric sites in the Songyuan area were arranged in this study at a spacing of 5 km. In addition, two intersecting magnetotelluric profiles, with a total of 23 measuring sites and a spacing of 2 km, were established near the Ningjiang earthquake swarm. Using a nonlinear conjugate gradient (NLCG) algorithm, resistivity structures in the lithosphere were obtained at different scales using three-dimensional (3D) inversion. The research results show that: a deep high-conductivity system (<10 Ω·m) was identified at 25–85 km depth in the lithosphere under Songyuan, corresponding closely to a region of high heat flow. It is inferred to be the molten material of mantle upwelling. In addition, a shallow high-conductivity system (<10 Ω·m) was identified beneath the Ningjiang earthquake swarm, which is interpreted to correspond to the Fuyu North fault. It is the main seismo-controlling structure of the Ningjiang earthquake swarm. The deep seismogenic environment and seismogenic mechanism of the Ningjiang earthquake swarm can be described as a deep upwelling of molten mantle material, which provides the power source. The deep magma intruded into the lower crust and accumulated, then intruded along faults and fissures, resulting in the activation of the North Fuyu fault and triggering the Ningjiang earthquake. It is attributed to the activation of shallow faults caused by the upwelling of molten mantle material. Full article
(This article belongs to the Special Issue Remote Sensing Makes it Possible: Prediction and Evaluation of Natural Hazards)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) The location of the study area. (<b>b</b>) The topographic map of the study area with the distribution of the MT stations (modified from [<a href="#B11-remotesensing-16-00547" class="html-bibr">11</a>]); (<b>c</b>) MT stations distribution near the Songyuan earthquake swarm. The earthquake data set was provided by the China Earthquake Networks Center, National Earthquake Data Center: <a href="http://data.earthquake.cn" target="_blank">http://data.earthquake.cn</a> (accessed on 28 May 2023). SSRF: Second Songhua River fault; F-ZF: Fuyu–Zhaodong fault; NFF: North Fuyu fault; D-BF: Dawa–Bohetai fault; K-DF: Keshan–Daan fault; HF: Honggang fault; GF: Gudian fault; CF: Chaganhua fault.</p> Full article ">Figure 2
<p>Distribution and characteristics of phase tensor ellipses for measuring sites in the Songyuan area.</p> Full article ">Figure 3
<p>Site-by-site RMS of large-scale and small-scale 3D inversion (refer to <a href="#remotesensing-16-00547-f001" class="html-fig">Figure 1</a> for fault names).</p> Full article ">Figure 4
<p>Comparison between the measured apparent resistivity curve and the inversion model apparent resistivity curve. XY and YX reflect the observed data, while X<sub>1</sub>Y<sub>1</sub> and Y<sub>1</sub>X<sub>1</sub> represents the modeled data.</p> Full article ">Figure 5
<p>Sensitivity test of the deep-resistivity structure in the preferred model.</p> Full article ">Figure 6
<p>Vertical slices of the small-scale 3D inversion model. Red dots represent the epicenter of the Ningjiang earthquake swarm, and black dots represent the MT stations involved in small-scale 3D inversion.</p> Full article ">Figure 7
<p>Large-scale 3D inversion slices at different depths (as marked). White circles represent the distribution of MT measuring sites, and black circles represent the distribution of epicenters projected to different depths.</p> Full article ">Figure 8
<p>(<b>a</b>) The position of the longitudinal section of the three-dimensional inversion result; (<b>b</b>) Deep magmatic system inferred from magnetotelluric 3D inversion in Songyuan area.</p> Full article ">Figure 9
<p>Illustration of molten material rising along the fracture. The subduction of the Paleo-Pacific plate led to the rise of molten material, which caused two sets of high-conductivity systems with different scales. The molten material migrated upward along the fault and activated the Fuyu North fault, thus inducing earthquakes.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-13
Back to TopTop